Multi-Dimensional Digital Twin Modeling for Fault Diagnosis of Industrial Centrifugal Pump
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Industrial centrifugal pumps operating under complex conditions frequently encounter failures, yet existing diagnostic methods face challenges in effectively fusing multi-source sensor data with physical mechanisms. To address these limitations, this paper proposes a multidimensional digital twin modeling approach that establishes a deep bidirectional synergy between data-driven perception and mechanism-based simulation. First, a Dynamic Sparse Spatio-Temporal Graph Attention Network (DS-STGAT) is designed to capture dynamic local and global dependencies among multi-source signals. Second, unlike conventional unidirectional methods, a novel data-mechanism collaborative adaptive mechanism is introduced. This creates a closed-loop pathway of “data-guided → simulation-refined → data-enhanced,” where the perception model retroactively optimizes simulation parameters (e.g., stiffness coefficients) via consistency constraints, while the simulation model provides physical priors to guide graph construction. Experimental results on multiple bearing datasets and real-world pump conditions demonstrate that the proposed method outperforms baseline models in accuracy, physical consistency, and robustness. Notably, the approach achieves high fault identification rates even in zero-shot scenarios, validating its effectiveness and scalability for the intelligent fault diagnosis of complex rotating machinery.
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